{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b9424635",
   "metadata": {},
   "source": [
    "# Federated Learning for Image Classification"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "73854d03",
   "metadata": {},
   "source": [
    ">The following codes are demos only. It's **NOT for production** due to system security concerns, please **DO NOT** use it directly in production."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "26887415",
   "metadata": {},
   "source": [
    "In this tutorial, we will use the image classification task to show how to complete the horizontal federated learning task in the `SecretFlow` framework.\n",
    "The `SecretFlow` framework provides a user-friendly API that makes it easy to apply your Keras or PyTorch model to a federated learning scenario as a federated learning model.\n",
    "In the rest of the tutorial we will show you how to turn your existing model into a federated model in `SecretFlow` to complete federated multi-party modeling tasks."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "223044ce",
   "metadata": {},
   "source": [
    "## What is Federated Learning"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "5b60fdb5",
   "metadata": {},
   "source": [
    "The federated learning discussed here is specifically focused on horizontal scenarios, where each participant shares the same business but reaches different customer groups. This allows samples from different parties to be combined to train a joint model with improved performance. An example of this scenario can be found in the medical field, where each hospital has its own distinctive patient group and hospitals in different regions largely do not overlap. However, their medical records (such as images and blood tests) for diagnostic purposes are of the same type."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "333698af",
   "metadata": {},
   "source": [
    "<img alt=\"federate_learning.png\" src=\"resources/federate_learning.png\" width=\"600\">"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "54c1b11a",
   "metadata": {},
   "source": [
    "Training process:  \n",
    "\n",
    "1. Each participant downloads the latest model from the server.\n",
    "2. Each participant uses its own local data to train the model, and uploads gradient encryption (or parameter encryption) to the server, which obtains the encryption gradient (encryption parameter) uploaded by all parties for security aggregation at the server, and updates model parameters with the aggregated gradient.\n",
    "3. The server returns the updated model to each participant.\n",
    "4. Each participant updates their local model, and prepare next training."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "65430f78",
   "metadata": {},
   "source": [
    "## Federated learning on SecretFlow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "59774353",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c5253753",
   "metadata": {},
   "source": [
    "Create 3 entities in the Secretflow environment [Alice, Bob, Charlie].\n",
    "Alice, Bob and Charlie are the three PYUs.\n",
    "Alice and Bob to be the clients and Charlie to be the server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "93f79d14",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The version of SecretFlow: 1.5.0.dev20240304\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-05 09:57:07,464\tINFO worker.py:1724 -- Started a local Ray instance.\n"
     ]
    }
   ],
   "source": [
    "import secretflow as sf\n",
    "\n",
    "# Check the version of your SecretFlow\n",
    "print('The version of SecretFlow: {}'.format(sf.__version__))\n",
    "\n",
    "# In case you have a running secretflow runtime already.\n",
    "sf.shutdown()\n",
    "\n",
    "sf.init(['alice', 'bob', 'charlie'], address='local')\n",
    "alice, bob, charlie = sf.PYU('alice'), sf.PYU('bob'), sf.PYU('charlie')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bf4e1f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "spu = sf.SPU(sf.utils.testing.cluster_def(['alice', 'bob']))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c53224e0",
   "metadata": {},
   "source": [
    "### Prepare Data\n",
    "\n",
    "Alice and Bob each own half the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "44a81605",
   "metadata": {},
   "outputs": [],
   "source": [
    "from secretflow.utils.simulation.datasets import load_mnist\n",
    "\n",
    "(x_train, y_train), (x_test, y_test) = load_mnist(\n",
    "    parts={alice: 0.15, bob: 0.85},\n",
    "    normalized_x=True,\n",
    "    categorical_y=True,\n",
    "    is_torch=False,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "23fbbbdf",
   "metadata": {},
   "source": [
    "`x_train`, `y_train`, `x_test`, `y_test` are both `FedNdarray`. Let's take a look at the data obtained from FedNdarray. FedNdarray is a virtual Ndarray built on a multi-party concept to protect data privacy.\n",
    "The underlying data is stored in each participant. The FedNdarray operation is actually performed by each participant on their own local data. The server or other clients do not touch the original data.\n",
    "For demonstration purposes, we will manually download the data to the driver.\n",
    "**This data will be used later in the unilateral model comparison**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5984479d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from secretflow.utils.simulation.datasets import dataset\n",
    "\n",
    "mnist = np.load(dataset('mnist'), allow_pickle=True)\n",
    "image = mnist['x_train']\n",
    "label = mnist['y_train']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e01bc559",
   "metadata": {},
   "source": [
    "Let's grab some samples from the data set, and just visually see, what does the data look like for Both Alice and Bob?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "031dbe50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "figure = plt.figure(figsize=(20, 4))\n",
    "j = 0\n",
    "\n",
    "for example in image[:40]:\n",
    "    plt.subplot(4, 10, j + 1)\n",
    "    plt.imshow(example, cmap='gray', aspect='equal')\n",
    "    plt.axis('off')\n",
    "    j += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "11098cb0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x400 with 40 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure = plt.figure(figsize=(20, 4))\n",
    "j = 0\n",
    "for example in image[10000:10040]:\n",
    "    plt.subplot(4, 10, j + 1)\n",
    "    plt.imshow(example, cmap='gray', aspect='equal')\n",
    "    plt.axis('off')\n",
    "    j += 1"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "8ff562fe",
   "metadata": {},
   "source": [
    "It can be seen from the above two examples that the data types and tasks of Alice and Bob are consistent, but the samples are different due to the different user groups they reach."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "806779e1",
   "metadata": {},
   "source": [
    "### Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25fbec5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_conv_model(input_shape, num_classes, name='model'):\n",
    "    def create_model():\n",
    "        from tensorflow import keras\n",
    "        from tensorflow.keras import layers\n",
    "\n",
    "        # Create model\n",
    "        model = keras.Sequential(\n",
    "            [\n",
    "                keras.Input(shape=input_shape),\n",
    "                layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "                layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "                layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "                layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "                layers.Flatten(),\n",
    "                layers.Dropout(0.5),\n",
    "                layers.Dense(num_classes, activation=\"softmax\"),\n",
    "            ]\n",
    "        )\n",
    "        # Compile model\n",
    "        model.compile(\n",
    "            loss='categorical_crossentropy', optimizer='adam', metrics=[\"accuracy\"]\n",
    "        )\n",
    "        return model\n",
    "\n",
    "    return create_model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "33fce574",
   "metadata": {},
   "source": [
    "### Training FL Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ac7e3cb8",
   "metadata": {},
   "source": [
    "1. Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c2c68047",
   "metadata": {},
   "outputs": [],
   "source": [
    "from secretflow.security.aggregation import SPUAggregator, SecureAggregator\n",
    "from secretflow.ml.nn import FLModel"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "37f25ac1",
   "metadata": {},
   "source": [
    "2. Define Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "25a3f788",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_classes = 10\n",
    "input_shape = (28, 28, 1)\n",
    "model = create_conv_model(input_shape, num_classes)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "564ac93b",
   "metadata": {},
   "source": [
    "3. Define the device list for participating training, which is the PYUS of each participant prepared previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a7955558",
   "metadata": {},
   "outputs": [],
   "source": [
    "device_list = [alice, bob]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "95051611",
   "metadata": {},
   "source": [
    "4. Define Aggregator  \n",
    "The SecretFlow framework provides a variety of aggregation schemes, including `SecureAggregator` and `PPUAggregator`, which can be used for secure aggregation. To learn more information about aggregation, see [Secure Aggregator](../developer/algorithm/secure_aggregation.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cfd83d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Create proxy actor <class 'secretflow.security.aggregation.secure_aggregator._Masker'> with party alice.\n",
      "INFO:root:Create proxy actor <class 'secretflow.security.aggregation.secure_aggregator._Masker'> with party bob.\n"
     ]
    }
   ],
   "source": [
    "secure_aggregator = SecureAggregator(charlie, [alice, bob])\n",
    "spu_aggregator = SPUAggregator(spu)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "4bb9c29c",
   "metadata": {},
   "source": [
    "5. Define FLModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1d4fbc7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-03-05 09:57:11.789182: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11/lib64:\n",
      "2024-03-05 09:57:11.789267: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11/lib64:\n",
      "2024-03-05 09:57:11.789274: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
      "2024-03-05 09:57:12.376683: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudnn.so.8'; dlerror: libcudnn.so.8: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11/lib64:\n",
      "2024-03-05 09:57:12.376712: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1934] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n",
      "INFO:root:Create proxy actor <class 'secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_w.PYUFedAvgW'> with party alice.\n",
      "INFO:root:Create proxy actor <class 'secretflow.ml.nn.fl.backend.tensorflow.strategy.fed_avg_w.PYUFedAvgW'> with party bob.\n"
     ]
    }
   ],
   "source": [
    "fed_model = FLModel(\n",
    "    server=charlie,\n",
    "    device_list=device_list,\n",
    "    model=model,\n",
    "    aggregator=secure_aggregator,\n",
    "    strategy=\"fed_avg_w\",\n",
    "    backend=\"tensorflow\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "e41f0a4c",
   "metadata": {},
   "source": [
    "6. Lets run model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e5bcb2d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:FL Train Params: {'x': FedNdarray(partitions={PYURuntime(alice): <secretflow.device.device.pyu.PYUObject object at 0x7feba8a66f50>, PYURuntime(bob): <secretflow.device.device.pyu.PYUObject object at 0x7feba8a66fe0>}, partition_way=<PartitionWay.HORIZONTAL: 'horizontal'>), 'y': FedNdarray(partitions={PYURuntime(alice): <secretflow.device.device.pyu.PYUObject object at 0x7feba8a66c50>, PYURuntime(bob): <secretflow.device.device.pyu.PYUObject object at 0x7feba8a673d0>}, partition_way=<PartitionWay.HORIZONTAL: 'horizontal'>), 'batch_size': 128, 'batch_sampling_rate': None, 'epochs': 10, 'verbose': 1, 'callbacks': None, 'validation_data': (FedNdarray(partitions={PYURuntime(alice): <secretflow.device.device.pyu.PYUObject object at 0x7feba8a67820>, PYURuntime(bob): <secretflow.device.device.pyu.PYUObject object at 0x7feba8a67b50>}, partition_way=<PartitionWay.HORIZONTAL: 'horizontal'>), FedNdarray(partitions={PYURuntime(alice): <secretflow.device.device.pyu.PYUObject object at 0x7feba8a67cd0>, PYURuntime(bob): <secretflow.device.device.pyu.PYUObject object at 0x7feba8a67ee0>}, partition_way=<PartitionWay.HORIZONTAL: 'horizontal'>)), 'shuffle': False, 'class_weight': None, 'sample_weight': None, 'validation_freq': 1, 'aggregate_freq': 1, 'label_decoder': None, 'max_batch_size': 20000, 'prefetch_buffer_size': None, 'sampler_method': 'batch', 'random_seed': 55916, 'dp_spent_step_freq': None, 'audit_log_dir': None, 'dataset_builder': None, 'wait_steps': 100, 'self': <secretflow.ml.nn.fl.fl_model.FLModel object at 0x7feaa9705090>}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[36m(pid=488924)\u001b[0m 2024-03-05 09:57:13.856657: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11/lib64:\n",
      "\u001b[36m(pid=488924)\u001b[0m 2024-03-05 09:57:13.856750: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda-11/lib64:\n",
      "\u001b[36m(pid=488924)\u001b[0m 2024-03-05 09:57:13.856759: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
      "\u001b[36m(pid=488924)\u001b[0m 2024-03-05 09:57:14.268949: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:08<00:00,  8.32it/s, {'loss': 0.99328053, 'accuracy': 0.7294833, 'val_loss': 0.2633847, 'val_accuracy': 0.9255}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 2/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:08<00:00,  8.02it/s, {'loss': 0.257982, 'accuracy': 0.9237857, 'val_loss': 0.1459615, 'val_accuracy': 0.9579}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 3/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:08<00:00,  8.06it/s, {'loss': 0.17152211, 'accuracy': 0.9489143, 'val_loss': 0.10869795, 'val_accuracy': 0.9691}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 4/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:08<00:00,  7.88it/s, {'loss': 0.1355056, 'accuracy': 0.95967144, 'val_loss': 0.08852749, 'val_accuracy': 0.9737}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 5/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:09<00:00,  7.74it/s, {'loss': 0.114622824, 'accuracy': 0.9655857, 'val_loss': 0.07427808, 'val_accuracy': 0.9769}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 6/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:09<00:00,  7.69it/s, {'loss': 0.102443844, 'accuracy': 0.96884286, 'val_loss': 0.06702591, 'val_accuracy': 0.9788}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 7/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:09<00:00,  7.41it/s, {'loss': 0.09362803, 'accuracy': 0.9713143, 'val_loss': 0.060703106, 'val_accuracy': 0.9805}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 8/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:09<00:00,  7.23it/s, {'loss': 0.08643787, 'accuracy': 0.97377145, 'val_loss': 0.05538711, 'val_accuracy': 0.9819}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 9/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:10<00:00,  6.99it/s, {'loss': 0.08027196, 'accuracy': 0.9755143, 'val_loss': 0.05230323, 'val_accuracy': 0.9833}]\n",
      "Train Processing: :   0%|          | 0/71 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Train Processing: :  99%|█████████▊| 70/71 [00:10<00:00,  6.97it/s, {'loss': 0.07390674, 'accuracy': 0.97742856, 'val_loss': 0.050366048, 'val_accuracy': 0.9832}]\n"
     ]
    }
   ],
   "source": [
    "history = fed_model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    validation_data=(x_test, y_test),\n",
    "    epochs=10,\n",
    "    sampler_method=\"batch\",\n",
    "    batch_size=128,\n",
    "    aggregate_freq=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fba7079a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Draw accuracy values for training & validation\n",
    "plt.plot(history[\"global_history\"]['accuracy'])\n",
    "plt.plot(history[\"global_history\"]['val_accuracy'])\n",
    "plt.title('FLModel accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Draw loss for training & validation\n",
    "plt.plot(history[\"global_history\"]['loss'])\n",
    "plt.plot(history[\"global_history\"]['val_loss'])\n",
    "plt.title('FLModel loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valid'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "94aa9e5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "([Mean(name='val_loss', total=503.6605, count=10000.0), Mean(name='val_accuracy', total=9832.0, count=10000.0)], {'alice': [Mean(name='val_loss', total=101.31542, count=1500.0), Mean(name='val_accuracy', total=1463.0, count=1500.0)], 'bob': [Mean(name='val_loss', total=402.34506, count=8500.0), Mean(name='val_accuracy', total=8369.0, count=8500.0)]})\n"
     ]
    }
   ],
   "source": [
    "global_metric = fed_model.evaluate(x_test, y_test, batch_size=128)\n",
    "print(global_metric)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "cb9c64ba",
   "metadata": {},
   "source": [
    "### Contrast experiment to local training"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a1309ed8",
   "metadata": {},
   "source": [
    "#### Model\n",
    "The model structure is consistent with the fl model above.\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "ef6bd7d5",
   "metadata": {},
   "source": [
    "#### Data\n",
    "Here, we only used data after a horizontal segmentation, with a total of 10,000 samples for `Alice`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3a239a07",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "\n",
    "\n",
    "def create_model():\n",
    "    model = keras.Sequential(\n",
    "        [\n",
    "            keras.Input(shape=input_shape),\n",
    "            layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"),\n",
    "            layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "            layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"),\n",
    "            layers.MaxPooling2D(pool_size=(2, 2)),\n",
    "            layers.Flatten(),\n",
    "            layers.Dropout(0.5),\n",
    "            layers.Dense(num_classes, activation=\"softmax\"),\n",
    "        ]\n",
    "    )\n",
    "    # Compile model\n",
    "    model.compile(\n",
    "        loss='categorical_crossentropy', optimizer='adam', metrics=[\"accuracy\"]\n",
    "    )\n",
    "    return model\n",
    "\n",
    "\n",
    "single_model = create_model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8d7bb14e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['x_test', 'x_train', 'y_train', 'y_test']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mnist.files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "fbd979bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "\n",
    "alice_x = image[:10000]\n",
    "alice_y = label[:10000]\n",
    "alice_y = OneHotEncoder(sparse_output=False).fit_transform(alice_y.reshape(-1, 1))\n",
    "\n",
    "random_seed = 1234\n",
    "alice_X_train, alice_X_test, alice_y_train, alice_y_test = train_test_split(\n",
    "    alice_x, alice_y, test_size=0.1, random_state=random_seed\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "74524f78",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "13/71 [====>.........................] - ETA: 0s - loss: 21.6627 - accuracy: 0.2981"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "71/71 [==============================] - 1s 10ms/step - loss: 5.2871 - accuracy: 0.6010 - val_loss: 0.4098 - val_accuracy: 0.8790\n",
      "Epoch 2/10\n",
      "71/71 [==============================] - 1s 9ms/step - loss: 0.6213 - accuracy: 0.8232 - val_loss: 0.2889 - val_accuracy: 0.9150\n",
      "Epoch 3/10\n",
      "71/71 [==============================] - 1s 9ms/step - loss: 0.4237 - accuracy: 0.8791 - val_loss: 0.2061 - val_accuracy: 0.9370\n",
      "Epoch 4/10\n",
      "71/71 [==============================] - 1s 8ms/step - loss: 0.2990 - accuracy: 0.9128 - val_loss: 0.1734 - val_accuracy: 0.9510\n",
      "Epoch 5/10\n",
      "71/71 [==============================] - 1s 8ms/step - loss: 0.2565 - accuracy: 0.9256 - val_loss: 0.1437 - val_accuracy: 0.9560\n",
      "Epoch 6/10\n",
      "71/71 [==============================] - 1s 9ms/step - loss: 0.2122 - accuracy: 0.9352 - val_loss: 0.1215 - val_accuracy: 0.9630\n",
      "Epoch 7/10\n",
      "71/71 [==============================] - 1s 8ms/step - loss: 0.1753 - accuracy: 0.9450 - val_loss: 0.1282 - val_accuracy: 0.9620\n",
      "Epoch 8/10\n",
      "71/71 [==============================] - 1s 8ms/step - loss: 0.1635 - accuracy: 0.9514 - val_loss: 0.1085 - val_accuracy: 0.9700\n",
      "Epoch 9/10\n",
      "71/71 [==============================] - 1s 9ms/step - loss: 0.1434 - accuracy: 0.9552 - val_loss: 0.0977 - val_accuracy: 0.9700\n",
      "Epoch 10/10\n",
      "71/71 [==============================] - 1s 8ms/step - loss: 0.1375 - accuracy: 0.9562 - val_loss: 0.1029 - val_accuracy: 0.9690\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fd6dc54d390>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "single_model.fit(\n",
    "    alice_X_train,\n",
    "    alice_y_train,\n",
    "    validation_data=(alice_X_test, alice_y_test),\n",
    "    batch_size=128,\n",
    "    epochs=10,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fe5109f7",
   "metadata": {},
   "source": [
    "The two experiments above simulated a training problem in a typical horizontal federation scenario,  Alice and Bob have same type of data. Each side had only a portion of the sample, but the training objectives is the same. If Alice only uses her own data to train the model, could only obtain a model with an accuracy of 0.969. However, if Bob's data is combined, a model with an accuracy close to 0.983 can be obtained. In addition, the generalization performance of the model jointly trained with multi-party data will also be better."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c15e286a",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "* This tutorial introduces what federated learning is and how to perform horizontal federated learning in `secretFlow`.  \n",
    "* It can be seen from the experimental data that horizontal federation can improve the model effect by expanding the sample size and combining multi-party training.\n",
    "* This tutorial uses a SecureAggregator to demonstrate, and secretflow provides a variety of aggregation schemes，for more infomation, see [Secure Aggregation](../developer/algorithm/secure_aggregation.ipynb).\n",
    "* next, you can use your data or model to explore how to do federate learning.\n"
   ]
  }
 ],
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   "language": "python",
   "name": "python3"
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    "name": "ipython",
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 },
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}
